Readability and Suitability of COPD Consumer Information
Bibliographic record
Abstract
Background. Information leaflets have been shown to positively or negatively impact adherence, depending on their content. The objective of this study was to perform an appraisal of the consumer information provided in COPD inhaler monographs. Methods. COPD inhalers were identified from the Health Canada Drug Product Database. Medication information and instructions for inhaler use were analyzed for readability by seven formulas, with an acceptability threshold of grades 6–8. Three researchers rated suitability using a modified Suitability Assessment of Materials (SAM) tool and assessed leaflets for explicit warnings. Results. Twenty-six inhalers with a COPD indication were evaluated. Medication information sections were rated as “difficult to read” or “hard,” and 85% (22/26) had a reading level above grade 8. The instructions for inhaler use were rated as “easy” or “fairly easy” to read and 63% (16/26) met the threshold by all formulas. While all leaflets achieved superior suitability ratings, extreme warnings included risk of premature death ( n=12 ), risks of serious injury ( n=26 ), serious interactions ( n=26 ), and statements that convey a serious consequence to therapy ( n=26 ). Conclusion. While COPD information leaflets in Canada performed well in terms of readability and suitability, overemphasis on side effects, warnings, and precautions may contribute to patient fear and nonadherence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".